Prime

The goal of reducing greenhouse gas emissions in Germany through the steady expansion of renewable energies and the associated increase in complexity will place the electricity grids facing significant challenges in the future.
 
Many of the tasks are based on probabilistic problems. Often, these can be approximated by deterministic considerations, e.g., mean and worst case. In essential tasks, however, the probabilistic problem area as a whole must be examined for strong statements. Such an investigation of the entire problem space is typically done by a Monte Carlo simulation. However, this process is very time-consuming and resource-consuming, and already in today's tasks often simplifications must be made, which limit the resilience of the results and in particular the ability to extrapolate the results.
 
A typical application for such probabilistic tasks in energy systems engineering is, for example, network expansion planning. The further transformation of the distribution grids into smart grids with more volatile generators, decentralized storage, and intelligent, active equipment in the electrical supply network leads to increasing uncertainty both
* In spatial planning: where do new plants originate?
* In quantity: how many new plants will there be?
* And in the schedule: What will the feed-in and demand characteristics of the systems look like concerning temporal gradients, as well as maximum and minimum values ​​in the future?
These and similar uncertainties must each be modeled by probability distributions, thus creating all potential scenarios for the development of renewable energy.
 
As a rule, many of these calculations will be redundant due to the same or very similar input data. Therefore, new, efficient probabilistic methods are needed to map the entire solution space for grid expansion planning. Therefore, in the project PrIME, methods for probabilistic tasks in energy system technology are to be considered and developed in a fundamentally oriented way. The method development should be based on typical probabilistic applications of energy system technology, to ensure high practical relevance for the results of basic research. Such methods then offer excellent application potential, both in network planning and in network operation management (for example, Day-Ahead Congestion Forecast, DACF).
 
As part of a consortium consisting of Fraunhofer IEE, the department e²n of the University of Kassel and several associated network operators, the Department of Intelligent Embedded Systems ensures the applicability and further development of the possible methods. The methods developed are used with different network calculation types such as load flow calculations, validated, evaluated and optimized.